Brains contain large number of neurons whose connections, formed by axonal and dendritic processes, are the structural underpinning of electrical circuits that control behavior. The analysis of circuits is of great importance. All acts of fine motor control, memory formation and cognition can only be understood if the circuitry within the brain compartments dealing with these functions is known. Likewise, the insight into psychiatric disease mechanisms and their pharmacological treatment requires brain circuitry to be known in detail. For example, recent findings suggest that diseases like autism or schizophrenia can be understood in terms of abnormalities in the micro- circuitry of the prefrontal cortex. We propose in this grant to develop and utilize bioinformatics tools that enable us to address circuitry in the Drosophila brain. The Drosophila central brain is formed by a stereotyped set of approximately 100 paired lineages, each one derived from one neuroblast. Neurons of one lineage form processes that spread within discrete compartments of the brain. Lineages thereby represent the most appropriate structural/developmental units of brain macro-circuitry. Reconstructing the projection of all lineages means to have generated an accurate map of Drosophila brain circuitry at the level of neuron populations (macro-circuitry). We propose to generate this map, presented in a standardized electronic format that is accessible to the neurobiology community. In addition, we will reconstruct circuitry at the level of individual synapses (micro-circuitry), which requires electron microscopy (EM). We have developed the software required for the automated recording, registration and navigation of large EM image data sets. We will further improve and use these tools to generate a digital EM dataset that, for the first time, encompasses the entire brain of an animal with a sizeable number of structurally complex neurons. Our software allows us to efficiently reconstruct the neural networks encountered in different parts of the brain. We anticipate that we will be able to learn structural principles about neural network that have general application.